Comparing Future Red Tide Scenarios

For this test run, I took the SEDAR Red Grouper stock assessment and used SSMSE to introduce future red tide events in the OM and EM. There were 6 total scenarios:

1. default: the base stock assessment with red tide events in 2005 and 2014.
   
2. red tide random 10: I used a custom function to generate 10 random red tide events in the projected years.
   
3. red tide random 25: I used a custom function to generate 25 random red tide events in the projected years.
   
4. red tide regular 3: I added a red tide event every 3 years.
5. red tide regular 5: I added a red tide event every 5 years.
6. red tide regular 5 mortality 5: I added a red tide event every 5 years and set the OM mortality to 0.5.

Import the results and summary files from the cloud.

Reviewing time series plots

List of the things we can plot with the ts_plot_variable function:

 [1] "year"        "Area"        "Seas"        "Bio_smry"    "SpawnBio"   
 [6] "Recruit_0"   "retainB_1"   "retainN_1"   "retainB_2"   "retainN_2"  
[11] "retainB_3"   "retainN_3"   "retainB_4"   "retainN_4"   "retainB_5"  
[16] "retainN_5"   "deadB_1"     "deadN_1"     "deadB_2"     "deadN_2"    
[21] "deadB_3"     "deadN_3"     "deadB_4"     "deadN_4"     "deadB_5"    
[26] "deadN_5"     "F_1"         "F_2"         "F_3"         "F_4"        
[31] "F_5"         "SPRratio"    "rec_dev"     "raw_rec_dev" "model_run"  
[36] "iteration"   "scenario"   

Here are the basic plots:

Red tide mortality

Red tide dead biomass

Spawning Stock Biomass

Retained Biomass

Long-Term SSB and Violin plots

These plots are similar to Doering et al. 2023

This plot takes the longest to process. It generates a plot of the SSB variation by scenario. Then it creates a series of violin plots with the OM and MS for Long-term average catch, Long-term catch variability, Short-term average catch, and Long-term average SSB.

Relative SSB

Catch Variability plot 1

Long-term Catch Variability plot

This plot is similar to the Long-term catch variability plot [2] in the previous chunk, but this produces a plot of the coefficient of variation instead of Catch (metric tons).

Final patchwork plot

This just combines the previous plots into a nice 2 by 2 patch work for easier viewing.

Reviewing derived quantities

Some of the same plots above but derived. Below is a list of all the derived quantity variables:

 [1] "Value.SSB"          "Value.Recr"         "Value.SPRratio"    
 [4] "Value.F"            "Value.Bratio"       "Value.ForeCatch"   
 [7] "Value.OFLCatch"     "Value.ForeCatchret" "Value.lnSPB"       
[10] "year"               "model_run"          "iteration"         
[13] "scenario"          

Recruitment

SSB

SPR Ratio

B Ratio

F

Term Plots

default

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

General Brainstorm:

Quantify how often the stock crashes from red tide events? Quantify the loss of catch from each red tide event, then the average loss.

 [1] "year"        "Area"        "Seas"        "Bio_smry"    "SpawnBio"   
 [6] "Recruit_0"   "retainB_1"   "retainN_1"   "retainB_2"   "retainN_2"  
[11] "retainB_3"   "retainN_3"   "retainB_4"   "retainN_4"   "retainB_5"  
[16] "retainN_5"   "deadB_1"     "deadN_1"     "deadB_2"     "deadN_2"    
[21] "deadB_3"     "deadN_3"     "deadB_4"     "deadN_4"     "deadB_5"    
[26] "deadN_5"     "F_1"         "F_2"         "F_3"         "F_4"        
[31] "F_5"         "SPRratio"    "rec_dev"     "raw_rec_dev" "model_run"  
[36] "iteration"   "scenario"   

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Fishing terminal year plots

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Terminal year with added median line

Only years with red tide events

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Fishing terminal year plots

I was redoing the median lines and sds, and noticed that the EM:OM ratio would be NA if the EM F is zero (happens a lot in F_5). I added those NAs back in as zeros and it dramatically dampens the F_5 “spike”. So I filtered this data to only years where a red tide occurred to highlight that the spike in the terminal year trend is only relevant to red tide years.

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

New complex Ratio plots

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

All years, biomass ratios

I was redoing the median lines and sds, and noticed that the EM:OM ratio would be NA if the EM F is zero (happens a lot in F_5). I added those NAs back in as zeros and it dramatically dampens the F_5 “spike”. So I filtered this data to only years where a red tide occurred to highlight that the spike in the terminal year trend is only relevant to red tide years.

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Red tide years, biomass ratio

This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Red tide years, biomass ratio, just Com and Rec Retained and Discards

This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Exploring Recruitment

Recruitment DQ

Recruitment TS

“rec_dev” “raw_rec_dev” “Recruit_0”

rec_dev TS

raw rec_dev TS

Recruitment ratios, all years

This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Terminal year recruitment ratios, all years

This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Terminal year recruitment ratios, red tide years

This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Terminal year SSB ratios, red tide years

This is the biomass ratios in just the red tide years so the trends are more clear in Fleet 5.

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

Inspired by Wetzel and Punt et al. 2011

I am going to attempt to make their Relative Error Plots.

RE = (E - T) / T

Variables of interest:

F_5, Recruit_0,

Recruitment

SpawnBio

Com F

Rec F

F_5

Warning: Removed 3670 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Com Catch

Rec Catch

Red Tide Discards

Warning: Removed 3670 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Warning: Removed 3670 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Removed 3670 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Warning: Removed 100 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Warning: Removed 3670 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Warning: Removed 3707 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Then I may attempt to calculate RMSE

F-Ratios over time, All years

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

F-Ratios over time, All years

10 random red tides

25 random red tides

Red tide every 3 years

Red tide every 5 years

Mortality = 0.5

F-Ratios over time, All years

10 random red tides

`geom_smooth()` using formula = 'y ~ x'

25 random red tides

`geom_smooth()` using formula = 'y ~ x'

Red tide every 3 years

`geom_smooth()` using formula = 'y ~ x'

Red tide every 5 years

`geom_smooth()` using formula = 'y ~ x'

Mortality = 0.5

`geom_smooth()` using formula = 'y ~ x'

Red Tide Magnitudes and Frequencies

The sums and counts of each red tide treatment to see which are comparable.

# A tibble: 6 × 4
  scenario                       n_years F_5_sum catch_sum
  <chr>                            <int>   <dbl>     <dbl>
1 red_tide_random_10                  10    153.  2005729.
2 red_tide_random_25                  25    432.  4054875.
3 red_tide_regular_3                  17    244.  2581966.
4 red_tide_regular_5                  10    159.  2134777.
5 red_tide_regular_5_mortality_5      10    399.  3789802.
6 default                             NA      0         0 

Selectivity Exploration

Attaching package: 'r4ss'

The following object is masked from 'package:stats':

    profile

The following object is masked from 'package:base':

    jitter

Ro Exploration

Extracted the R0 from SR_LN_R0 from the summary$scalar.